Construction And Application Of Surgical Scheduling Model For Elective Patients

With the continuous improvement of people’s awareness of physical health and the pursuit of high-quality medical environment and conditions,people have become more convinced of the level of surgery in Three-A hospitals,leading to the fact that most Three-A hospitals are overcrowded and are facing the problem of shortage of operation-related resources.Without the guarantee of surgical resources,patients’ health will be threatened,so "the difficulty of seeking medical treatment" has become a hot issue in the society.Based on the actual investigation,a study on the surgical scheduling of elective patients is carried out,and the main research work is as follows.(1)Based on the actual situation and literature research,this thesis integrates the factors of medical staff’s decision-making styles into the medical staff scheduling group,and comprehensively considers the available time of medical staff,the number of medical staff,as well as the number of doctors,nurses and anesthesiologists of each surgical team composed.A surgical team assignment model based on the influence of decision-making styles factors is established,so that the total service level of all the assigned surgical teams is the highest.Particle swarm optimization algorithm is used to solve the problem.The model is solved quickly and accurately.(2)On the basis of the assignment of the surgical team,the assignment result of the surgical team is used as the input of the following surgical scheduling model for elective patients.The practical factors such as the type of surgery,the level of required operating room and the cleaning time of operation are fully considered in the surgical scheduling of elective patients.A dual-objective mixed integer programming model is established considering the balance of service time of operating room and patient satisfaction.Considering the large data size,the accurate solution is inappropriate,and the particle swarm optimization and genetic algorithm are combined to form an improved genetic algorithm based on particle swarm optimization algorithm in this thesis.(3)As the performance of the algorithm is affected by the parameters in the algorithm,the operation effect of different parameters for the algorithm is tested and the optimal combination of the main influencing parameters to achieve the optimal performance of the algorithm is found.The model is solved by using the improved genetic algorithm based on particle swarm optimization in a small data scale.Compared with the accurate results,the error is small with a maximum error of 1.7%,which verifies the correctness of the model and the reliability of the algorithm.At the same time,the algorithm also has good convergence and diversity,which provides theoretical and methodological support for subsequent studies.(4)Based on the model proposed in this thesis and the improved algorithm,three simulation experiments are conducted on the actual case data of different scale of data of patients undergoing surgery in the laminar flow operating room.To avoid the occurrence of contingency in the operation results,the average value of multiple operations is taken as the final simulation result.Then the results are compared with the actual scheduling results of the hospital.In the horizontal comparison,the simulation scheduling results obtained in this thesis are better than the actual scheduling results.In vertical comparison,the improved effect is better with the increase of data size,which verifies the feasibility and effectiveness of the model and method established in this thesis.(5)The simulation is carried out through multiple-case data of different sizes to analyze how the manager could choose a complete solution with effective information about the grouping of medical staff,the date of patients’ surgery and the daily arrangement of each operating room from multiple schemes under different demands.The results of this study have important practical application value for hospital surgical scheduling.